import numpy as np
import matplotlib.pyplot as plt
from testCases import *
import sklearn
import sklearn.datasets
import sklearn.linear_model
from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets

np.random.seed(1)

X, Y = load_planar_dataset()
# plt.scatter(X[0, :], X[1, :], c=Y.reshape(X[0,:].shape), s=40, cmap=plt.cm.Spectral)
# # plt.show()

# shape_X = X.shape
# shape_Y = Y.shape
# m = shape_X[1]

# print ('The shape of X is: ' + str(shape_X))
# print ('The shape of Y is: ' + str(shape_Y))
# print ('I have m = %d training examples!' % (m))

# clf = sklearn.linear_model.LogisticRegressionCV()
# clf.fit(X.T, Y.T)

# plot_decision_boundary(lambda x: clf.predict(x), X, Y)
# plt.title("Logistic Regression")
#
# LR_predictions = clf.predict(X.T)
# print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +
#        '% ' + "(percentage of correctly labelled datapoints)")
# plt.show()

def layer_sizes(X, Y):
    n_x = X.shape[0]
    n_h = 4
    n_y = Y.shape[0]
    return (n_x, n_h, n_y)

# X_assess, Y_assess = layer_sizes_test_case()#X_assess: [5,3], Y_assess: [2,3]
# (n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)


# print("The size of the input layer is: n_x = " + str(n_x))
# print("The size of the hidden layer is: n_h = " + str(n_h))
# print("The size of the output layer is: n_y = " + str(n_y))

def initialize_parameters(n_x, n_h, n_y):
    np.random.seed(2)

    W1 = np.random.randn(n_h, n_x) * 0.01
    b1 = np.zeros((n_h, 1))
    W2 = np.random.randn(n_y, n_h) * 0.01
    b2 = np.zeros((n_y, 1))

    assert (W1.shape == (n_h, n_x))
    assert (b1.shape == (n_h, 1))
    assert (W2.shape == (n_y, n_h))
    assert (b2.shape == (n_y, 1))

    parameters = {"W1" : W1, "b1" : b1, "W2" : W2, "b2" : b2}
    return parameters

# n_x, n_h, n_y = initialize_parameters_test_case()
# parameters = initialize_parameters(n_x, n_h, n_y)
# print("W1 = " + str(parameters["W1"]))
# print("b1 = " + str(parameters["b1"]))
# print("W2 = " + str(parameters["W2"]))
# print("b2 = " + str(parameters["b2"]))

def forward_propagation(X, parameters):
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]

    Z1 = np.dot(W1, X) + b1
    A1 = np.tanh(Z1)
    Z2 = np.dot(W2, A1) + b2
    A2 = sigmoid(Z2)

    assert (A2.shape == (1, X.shape[1]))

    cache = {"Z1" : Z1, "A1" : A1, "Z2" : Z2, "A2" : A2}
    return A2, cache

# X_assess, parameters = forward_propagation_test_case()
# A2, cache = forward_propagation(X_assess, parameters)
# print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))

def compute_cost(A2, Y, parameters):
    m = Y.shape[1]
    logprobs = Y * np.log(A2) + (1 - Y) * np.log(1 - A2)
    cost = -1 / m * np.sum(logprobs)
    cost = np.squeeze(cost)

    assert (isinstance(cost, float))
    return cost

# A2, Y_assess, parameters = compute_cost_test_case()
# print("cost = " + str(compute_cost(A2, Y_assess, parameters)))

def backward_propagation(parameters, cache, X, Y):
    m = X.shape[1]

    W1 = parameters["W1"]
    W2 = parameters["W2"]


    A1 = cache["A1"]
    A2 = cache["A2"]

    dZ2 = A2 - Y
    dW2 = 1 / m * np.dot(dZ2, A1.T)
    db2 = 1 / m * np.sum(dZ2, axis=1, keepdims=True)
    dZ1 = np.dot(W2.T, dZ2) * (1 - np.power(A1, 2))
    dW1 = 1 / m * np.dot(dZ1, X.T)
    db1 = 1 / m * np.sum(dZ1, axis=1, keepdims=True)
    grads = {"dW1" : dW1, "db1" : db1, "dW2" : dW2, "db2" : db2}
    return grads

# parameters, cache, X_assess, Y_assess = backward_propagation_test_case()
# grads = backward_propagation(parameters, cache, X_assess, Y_assess)
# print ("dW1 = "+ str(grads["dW1"]))
# print ("db1 = "+ str(grads["db1"]))
# print ("dW2 = "+ str(grads["dW2"]))
# print ("db2 = "+ str(grads["db2"]))

def update_parameters(parameters, grads, learning_rate = 1.2):
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]

    dW1 = grads["dW1"]
    db1 = grads["db1"]
    dW2 = grads["dW2"]
    db2 = grads["db2"]

    W1 = W1 - learning_rate * dW1
    b1 = b1 - learning_rate * db1
    W2 = W2 - learning_rate * dW2
    b2 = b2 - learning_rate * db2

    parameters = {"W1" : W1, "b1" : b1, "W2" : W2, "b2" : b2}
    return parameters

# parameters, grads = update_parameters_test_case()
# parameters = update_parameters(parameters, grads)
# print("W1 = " + str(parameters["W1"]))
# print("b1 = " + str(parameters["b1"]))
# print("W2 = " + str(parameters["W2"]))
# print("b2 = " + str(parameters["b2"]))

def nn_model(X, Y, n_h, num_iterations = 10000, print_cost = False):
    np.random.seed(3)
    n_x = layer_sizes(X, Y)[0]
    n_y = layer_sizes(X, Y)[2]

    parameters = initialize_parameters(n_x, n_h, n_y)
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]

    for i in range(0, num_iterations):
        A2, cache = forward_propagation(X, parameters)
        cost = compute_cost(A2, Y, parameters)
        grads = backward_propagation(parameters, cache, X, Y)
        parameters = update_parameters(parameters, grads)

        if(print_cost and i % 1000 == 0):
            print("Cost after iteration %i: %f" %(i, cost))

    return parameters

# X_assess, Y_assess = nn_model_test_case()
# parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False)
# print("W1 = " + str(parameters["W1"]))
# print("b1 = " + str(parameters["b1"]))
# print("W2 = " + str(parameters["W2"]))
# print("b2 = " + str(parameters["b2"]))

def predict(parameters, X):
    A2, cache = forward_propagation(X, parameters)
    predictions = np.round(A2)

    return predictions

# parameters, X_assess = predict_test_case()
# predictions = predict(parameters, X_assess)
# print("predictions mean = " + str(np.mean(predictions)))

parameters = nn_model(X,Y,4,10000, True)
# plot_decision_boundary(lambda x : predict(parameters, x.T), X, Y)
# plt.title("Decision Boundary for hidden layer size " + str(4))
# plt.show()

# predictions = predict(parameters, X)
# print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')

plt.figure(figsize=(16, 32))
hidden_layer_sizes = [1, 2, 3, 4, 5, 10, 20,100]
for i, n_h in enumerate(hidden_layer_sizes):
    plt.subplot(5, 2, i+1)
    plt.title('Hidden Layer of size %d' % n_h)
    # plt.show()
    parameters = nn_model(X, Y, n_h, num_iterations = 5000)
    plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)
    predictions = predict(parameters, X)
    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)
    print ("Accuracy for {} hidden units: {} %".format(n_h, accuracy))
plt.show()